32 GenAI in Banking & Finance :Understanding Answer Relevancy Metrics

Understanding Answer Relevancy Metrics: A Comprehensive Guide

Answer relevancy stands as one of the most critical dimensions of generative AI system performance, yet it remains frequently misunderstood or underutilized in quality assurance frameworks. Unlike metrics that measure factual accuracy or grammatical correctness, answer relevancy specifically addresses whether a generated response actually addresses the user's underlying intent and question. This distinction has profound implications for real-world deployment, particularly in customer-facing applications where irrelevant but well-written answers create significant friction and erode user trust.

What Answer Relevancy Actually Measures

Answer Relevancy evaluates the alignment between a generated response and the original user query, assessing whether the system stayed focused on addressing the actual question rather than wandering into tangential or off-topic territory. The metric operates on a fundamental principle: a response can be factually correct, grammatically perfect, and thoroughly researched—yet still fail if it doesn't address what the user actually needed to know.​

The distinction matters deeply in real-world systems. Consider a practical scenario: a user asks their company's AI chatbot, "What's our discount policy for enterprise customers?" An irrelevant answer might provide a comprehensive overview of the company's entire discount structure without the specific approval thresholds and process steps the user needs to execute their workflow immediately. The system has provided information, but it has failed on relevancy.​

How Answer Relevancy Works: The Technical Foundation

Reverse-Engineering Questions from Answers

The most widely adopted approach to measuring answer relevancy, implemented in frameworks like RAGAS (RAG Assessment), uses an elegant reversal technique. Rather than directly comparing the answer to the question—which can be computationally expensive and semantically unreliable—the system generates multiple artificial questions based on the answer itself. If the system can generate questions from an answer that closely resemble the original question, this suggests the answer contains the information that would naturally elicit that original question.​

The calculation follows a straightforward two-step process:

Step 1: Question Generation — An LLM generates three or more hypothetical questions that someone might ask to elicit the provided answer. For example, if an answer discusses France's location in Western Europe and Paris as its capital, the system might generate: "Where is France located?" "What is France's capital city?" or "Name a European country and its capital."

Step 2: Semantic Similarity Computation — The system calculates cosine similarity between the embeddings of the original question and each generated question, then averages these scores. This produces a relevancy score typically between 0 and 1, with higher scores indicating stronger alignment.​

The mathematical elegance of this approach lies in its independence from specific wording. Two questions asked in completely different ways but carrying identical intent will produce high similarity scores, while a question and an answer about a tangential topic will produce low scores.

LLM-as-a-Judge: The Alternative Approach

A parallel methodology treats the evaluation problem as a classification task where a specialized LLM acts as a judge. Rather than computing embeddings and similarity scores, this approach uses carefully crafted prompts to have an evaluator model independently assess: Does this response answer the question? Is the response focused or does it contain significant off-topic content?​

The LLM-as-a-Judge approach offers particular advantages when answers are complex, involve multi-step reasoning, or span multiple documents. Contemporary implementations show that LLM judges often align more closely with human judgments than humans agree with each other—a counterintuitive but well-documented phenomenon in recent research. The judge's task is framed as a simpler classification problem rather than attempting to generate matching content, which leverages different capabilities of the model.​

Key Considerations for Implementation

Balancing Completeness and Focus

Answer Relevancy metrics must navigate the tension between rewarding comprehensive responses and penalizing unnecessary elaboration. A response about meditation benefits that provides both physical and mental health advantages scores higher than one addressing only physical benefits—assuming the original question asked for "benefits" broadly. However, the same comprehensive response would score lower if the original question specifically asked for physical benefits only.​

Real-world systems must calibrate this balance based on domain requirements. Enterprise legal document retrieval demands extremely precise, relevant answers with minimal tangential information. General knowledge assistants, by contrast, benefit from broader contextual background.

Detecting and Distinguishing Off-Topic Content

The metric identifies answers that drift into unrelated topics, distinguishing between two failure modes:

1. Incomplete Answers — Responses that address part of the question but omit critical information
2. Tangential Responses — Answers that remain topically related but don't directly address the specific question asked

A response about meditation's philosophical history scores lower when asked about meditation benefits, even if philosophically interesting, because it demonstrates topic drift. Conversely, a response about meditation's benefits plus a brief historical context would likely maintain strong relevancy scores since the primary content addresses the question.​

Distinguishing Relevancy from Factuality

A critical implementation detail: Answer Relevancy metrics do not evaluate whether information is factually correct. A system can generate an answer highly relevant to a question—one that directly, comprehensively, and precisely addresses what was asked—while containing completely false information. This separation of concerns requires complementary evaluation metrics (faithfulness metrics assess factual grounding in source material; semantic answer similarity compares against ground-truth reference answers).​

This distinction has profound implications for production monitoring. A low relevancy score indicates an off-topic or incomplete response requiring architectural changes (perhaps retrieval failures, prompt tuning, or query understanding improvements). A low faithfulness score or semantic similarity score indicates hallucination or factual accuracy issues—a different set of remediation strategies.

Scoring Interpretation and Real-World Application

0.0-0.3:  Very low relevancy: Response substantially fails to address the question; likely explores tangential topics or fundamentally misunderstands the query

0.3-0.6: Low to moderate relevancy: Response addresses some aspects of the question but contains significant off-topic content or notable omissions

0.6-0.8: Moderate to high relevancy: Response directly addresses the question with minor tangential content or small gaps in completeness

0.8-1.0: High relevancy: Response comprehensively and precisely addresses the question with minimal irrelevant information

In practice, acceptable thresholds vary by use case. Healthcare systems providing diagnostic guidance typically require scores above 0.85, accepting the possibility of missing context in exchange for absolute focus on the specific patient situation. General knowledge systems might operate effectively at 0.70 thresholds, accommodating broader contextual explanation.​

Real-World Example: The Meditation Query

The foundational example illustrates the metric's practical value:

Question: "What are the benefits of meditation?"

Answer (Low Relevancy): "Meditation originated in ancient Hindu and Buddhist traditions around 5,000 years ago. Monks in monasteries practiced silent meditation for spiritual enlightenment."

This answer is historically accurate and topically related but fundamentally fails to answer what was asked. The reverse-engineered questions would likely include: "What is the history of meditation?" or "Where did meditation originate?" These differ substantially from the original question, producing a low relevancy score (likely 0.3-0.5).

Answer (High Relevancy): "Meditation provides numerous benefits including reduced stress and anxiety, improved emotional regulation, enhanced focus and concentration, better sleep quality, and cardiovascular health improvements. Regular practice strengthens the prefrontal cortex responsible for decision-making and emotional control."

The reverse-engineered questions would include: "What benefits does meditation provide?" and "How does meditation improve mental health?" These closely mirror the original question, producing high relevancy scores (likely 0.85-0.95).

Conclude:

Answer relevancy remains fundamental to generative AI quality assurance precisely because it addresses a deceptively simple question with profound implications: Did the system actually answer the question? Building systematic evaluation around this metric ensures AI systems serve user needs effectively rather than impressively answering questions that were never asked.

✍️ Author’s Note

This blog reflects the author’s personal point of view — shaped by 22+ years of industry experience, along with a deep passion for continuous learning and teaching.
The content has been phrased and structured using Generative AI tools, with the intent to make it engaging, accessible, and insightful for a broader audience.